Data-Nash
The speaker in the YouTube video titled "How I'd Learn Data Science In 2023 (If I Could Restart) | A Beginner's Roadmap" discusses their approach to learning data science in 2023 from scratch. They prioritize understanding what data science is and its business applications before moving into coding with Python, working with data types, functions, libraries, and doing projects. The speaker emphasizes project-based learning, improving math skills, reading documentation, and learning machine learning algorithms. The speaker also highlights the importance of creating a good portfolio with 2 or 3 in-depth projects, learning SQL, and tailoring CVs to job requirements. Ultimately, the speaker encourages learners to customize their approach to fit their preferences and career goals.
In this section, the speaker shares how they would approach learning data science in 2023 if they had to start from scratch. The first step they recommend is to take the time to truly understand what data science is and how it's used by businesses, rather than jumping straight into coding. Then, the speaker suggests learning Python, with data types being the first thing to focus on, followed by working with data frames, loops, functions, and libraries such as Pandas and NumPy. With this baseline level of Python, the speaker recommends doing a project to apply what has been learned so far.
In this section, the speaker emphasizes the importance of doing projects when learning data science and not just relying on tutorials. They suggest a simple project to start with, such as coding a function that checks whether a data frame has a sales and website traffic column and divides those two to get the conversion rate. Additionally, the speaker stresses the importance of having a foundation in mathematics, which can be improved with practice. They suggest doing more in-depth projects on Kaggle or watching long-form tutorials on YouTube to gain experience. The speaker also advises learning how to read documentation for libraries and familiarizing oneself with APIs. Finally, they suggest learning the basics of machine learning algorithms.
In this section, the speaker discusses the importance of creating a good portfolio with 2 or 3 in-depth projects for a beginner's roadmap in data science. Having a portfolio that is expertly done can help to showcase your skills to potential employers. The speaker also stresses the usefulness of learning SQL and recommends putting it on your CV regardless of whether it is required for the job or not. Additionally, the speaker advises learners to tailor their CVs to the job they want and prioritize relevant experiences near the top. Finally, the speaker emphasizes there is no one-size-fits-all solution and encourages learners to tweak their approach to suit their preferences and career goals.
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